Public Health and Primary Health Care Collaboration in Eight High-Income Countries During the Covid-19 Pandemic
Bibliographic record
Abstract
CONTEXT: The COVID-19 pandemic highlights the importance of strong public health (PH) and primary health care (PHC) systems to respond nimbly and effectively during times of crisis. Both play a crucial role in triage and prevention, management, vaccination, and communication. PH and PHC systems, however, often act in parallel streams, but rarely together. OBJECTIVE: This study aims to describe PH and PHC collaboration during the COVID-19 pandemic in eight high-income countries. METHODS: In-depth case study reports were generated for each country or jurisdiction. Reports searched both peer-review publications and grey literature on five dimensions identified by the World Health Organization regarding COVID-19 management. Reports included country-specific health system descriptions, PH and PHC actions during the pandemic, and an evaluation of strengths and weaknesses. Expert validation was conducted by internal country stakeholders prior to cross-jurisdiction analyses. ANALYSIS: Thematic content analysis was conducted on all reports to develop a coding framework. Codes were identified that were relevant to the research questions. The study team discussed and reconciled discrepancies in themes until consensus was reached. RESULTS: Data was collected from eight high-income countries (Belgium, Canada, Germany, Italy, Japan, the Netherlands, Norway, and Spain) from March 2020 to July 2021. Four key themes were identified along with respective strengths/weaknesses. 1) Health information systems: this played a critical role for disease containment and management when designed for efficient data management and cross-sectoral data-sharing. 2) Communication: In countries where PHC was engaged early on, PH messages were amplified; in other countries, a lack of cohesion in communication resulted in poor or delayed community-level responses. 3) Human resource capacity: Health human resources were overwhelmed, with many staff redeployed and undertrained. 4) Professional training: Health professionals who received dual training in PH and PHC acted as strong community champions and may be a bridge for future pandemics. CONCLUSION: Health system needs shifted dramatically throughout the COVID-19 pandemic. Our findings highlight four key lessons regarding PH and PHC collaboration from eight high-income countries. Future pandemic preparedness should focus on health information systems and data management, PH communication, health human resources, and education and training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".